pix2pix网络结构代码

pix2pix是一种用于图像到图像的转换的深度学习网络，它可以将一种类型的图像转换为另一种类型的图像。下面是一个基于TensorFlow的pix2pix网络结构的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model

def downsample(filters, size, apply_batchnorm=True):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = Sequential()
    result.add(Conv2D(filters, size, strides=2, padding='same', kernel_initializer=initializer, use_bias=False))

    if apply_batchnorm:
        result.add(BatchNormalization())

    result.add(LeakyReLU())

    return result

def upsample(filters, size, apply_dropout=False):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = Sequential()
    result.add(Conv2DTranspose(filters, size, strides=2, padding='same', kernel_initializer=initializer, use_bias=False))

    result.add(BatchNormalization())

    if apply_dropout:
        result.add(Dropout(0.5))

    result.add(ReLU())

    return result

def Generator():
    inputs = tf.keras.layers.Input(shape=[256, 256, 3])

    down_stack = [
        downsample(64, 4, apply_batchnorm=False),
        downsample(128, 4),
        downsample(256, 4),
        downsample(512, 4),
        downsample(512, 4),
        downsample(512, 4),
        downsample(512, 4),
        downsample(512, 4),
    ]

    up_stack = [
        upsample(512, 4, apply_dropout=True),
        upsample(512, 4, apply_dropout=True),
        upsample(512, 4, apply_dropout=True),
        upsample(512, 4),
        upsample(256, 4),
        upsample(128, 4),
        upsample(64, 4),
    ]

    initializer = tf.random_normal_initializer(0., 0.02)
    last = Conv2DTranspose(3, 4, strides=2, padding='same', kernel_initializer=initializer)

    x = inputs

    skips = []
    for down in down_stack:
        x = down(x)
        skips.append(x)

    skips = reversed(skips[:-1])

    for up, skip in zip(up_stack, skips):
        x = up(x)
        x = Concatenate()([x, skip])

    x = last(x)

    return Model(inputs=inputs, outputs=x)

def Discriminator():
    initializer = tf.random_normal_initializer(0., 0.02)

    inp = Input(shape=[256, 256, 3], name='input_image')
    tar = Input(shape=[256, 256, 3], name='target_image')

    x = concatenate([inp, tar])

    down1 = downsample(64, 4, False)(x)
    down2 = downsample(128, 4)(down1)
    down3 = downsample(256, 4)(down2)

    zero_pad1 = ZeroPadding2D()(down3)
    conv = Conv2D(512, 4, strides=1, kernel_initializer=initializer, use_bias=False)(zero_pad1)
    batchnorm1 = BatchNormalization()(conv)
    leaky_relu = LeakyReLU()(batchnorm1)
    zero_pad2 = ZeroPadding2D()(leaky_relu)

    last = Conv2D(1, 4, strides=1, kernel_initializer=initializer)(zero_pad2)

    return tf.keras.Model(inputs=[inp, tar], outputs=last)

这是一个简化版的pix2pix网络结构，你可以根据需要进行修改和扩展。在这个示例中，Generator和Discriminator都是基于Convolutional Neural Network (CNN) 构建的。